electricsheepeurope/europe-ilo-luu-xlu2-sex-dsb-rt-combined-rate-of-time-related-underemployment-and

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 1K<n<10K tags: - tabular - europe - ilostat - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Combined rate of time-related underemployment and unemployment (LU2) by sex and disability | Europe (ILOSTAT)" --- # Combined rate of time-related underemployment and unemployment (LU2) by sex and disability | Europe (ILOSTAT) 🇪🇺 **4,512 observations** · **31 Europe countries** · **2004–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **4,512 observations** of `Other measures of labour underutilization` data across **31 Europe countries**, spanning **2004–2025**, covering **1 distinct indicators**. ## About the source **ILOSTAT** is the ILO's central statistics database, the leading global source for labour statistics. It compiles indicators across employment, unemployment, wages, working time, child labour, informal economy, social protection, occupational injuries, and SDG decent work targets — drawing on national labour force surveys, household income surveys, establishment surveys, and administrative records. Coverage spans 200+ economies, with the ILO's Department of Statistics responsible for harmonisation. - **Source:** [ILOSTAT](https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU2_SEX_DSB_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Other measures of labour underutilization ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=LUU_XLU2_SEX_DSB_RT` and filtered to Europe ISO3 country codes. ILOSTAT harmonises raw survey microdata using ICLS (International Conference of Labour Statisticians) definitions; sources are flagged in the `source.label` column for traceability. ## Geographic coverage 31 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GBR` | 189 | 2005 | 2025 | | `BEL` | 162 | 2004 | 2023 | | `AUT` | 162 | 2004 | 2023 | | `FIN` | 162 | 2004 | 2023 | | `FRA` | 162 | 2004 | 2023 | | `ESP` | 162 | 2004 | 2023 | | `GRC` | 162 | 2004 | 2023 | | `PRT` | 162 | 2004 | 2023 | | `ITA` | 162 | 2004 | 2023 | | `EST` | 161 | 2004 | 2023 | | `LUX` | 161 | 2004 | 2023 | | `SWE` | 160 | 2004 | 2023 | | `CZE` | 153 | 2005 | 2023 | | `HUN` | 153 | 2005 | 2023 | | `SVN` | 153 | 2005 | 2023 | | ... | _16 more countries_ | | | ## Indicators (sample) - `LUU_XLU2_SEX_DSB_RT` — Combined rate of time-related underemployment and unemployment (LU2) by sex and disability status (%) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `AUT` | | `ref_area.label` | `string` | Country name in English | `Austria` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BB:275` | | `source.label` | `string` | Source name in English | `HIES - EU Statistics on Income and Li…` | | `indicator` | `string` | ILOSTAT indicator code | `LUU_XLU2_SEX_DSB_RT` | | `indicator.label` | `string` | Indicator name in English | `Combined rate of time-related underem…` | | `sex` | `string` | Disaggregation by sex (SEX_T = total, SEX_M = male, SEX_F = female) | `SEX_T` | | `sex.label` | `string` | — | `Total` | | `classif1` | `string` | First classification variable (age, education, status, etc.) | `DSB_STATUS_TOTAL` | | `classif1.label` | `string` | — | `Disability status: Total` | | `time` | `int64` | Observation year | `2023` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `7.745` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C14:6260` | | `note_classif.label` | `string` | — | `Nonstandard definition of disability:…` | | `note_indicator` | `string` | — | `I11:264` | | `note_indicator.label` | `string` | — | `Break in series: Methodology revised` | | `note_source` | `string` | — | `R1:3513_T2:85` | | `note_source.label` | `string` | — | `Repository: ILO-STATISTICS - Micro da…` | ## Disaggregation dimensions The following columns provide disaggregation dimensions: - **`sex`** (3 unique values): `SEX_T`, `SEX_M`, `SEX_F` ## Data quality & caveats - Data is annual frequency. Some indicators also publish monthly or quarterly series — those are not included here. - When an indicator has multiple sources for the same country×year, the ILO-selected 'best source' is used. - Disaggregation columns (`sex`, `classif1`, `classif2`) are non-null only when the indicator publishes that breakdown. ## Usage ```python from datasets import load_dataset ds = load_dataset("electricsheepeurope/europe-ilo-luu-xlu2-sex-dsb-rt-combined-rate-of-time-related-underemployment-and") df = ds["train"].to_pandas() print(df.head()) ``` ### Filter to one country ```python germany = df[df["ref_area"] == "DEU"] ``` ### Time-series for a single indicator ```python sample = (df[df["indicator"] == "LUU_XLU2_SEX_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU2_SEX_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU2_SEX_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_luu_xlu2_sex_dsb_rt_combined_rate_of_time_related_underemployment_and_2025, title = {Combined rate of time-related underemployment and unemployment (LU2) by sex and disability | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU2_SEX_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-luu-xlu2-sex-dsb-rt-combined-rate-of-time-related-underemployment-and}} } ``` ## License Released under [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/). Original data © International Labour Organization (ILO). When using this dataset, please cite both the original source above and the Electric Sheep Europe repackaging. ## About Electric Sheep Electric Sheep Europe is part of the Electric Sheep mission: a unified, ML-ready data layer for Europe on HuggingFace. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use `load_dataset()` to start working in seconds. Browse the full collection: [huggingface.co/electricsheepeurope](https://huggingface.co/electricsheepeurope) --- _Provenance: ingested 2026-05-27 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU2_SEX_DSB_RT_This dataset contains 4,512 observations across 31 European countries from 2004 to 2025, covering one distinct indicator: the combined rate of time-related underemployment and unemployment (LU2) by sex and disability status (%). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via its REST API and filtered for European countries. The dataset includes columns such as country code, country name, indicator code, indicator name, sex disaggregation, disability status classification, observation year, observed value, observation status, etc., making it suitable for natural language processing tasks like tabular classification, regression, and time-series forecasting. The data is harmonized by ILO based on International Conference of Labour Statisticians (ICLS) definitions, with source flags and quality caveats for traceability and reliability.

搜集汇总

数据集介绍

构建方式

该数据集源自国际劳工组织（ILO）的ILOSTAT数据库，聚焦于欧洲地区按性别和残疾状况划分的时间相关就业不足与失业综合率（LU2）。数据通过ILOSTAT REST API直接获取，并利用国际劳工统计学家会议（ICLS）定义的标准化方法对原始调查微观数据进行协调处理。随后依据欧洲ISO3国家代码进行地理筛选，最终整合为包含4,512条观测记录的表格型数据集，覆盖31个欧洲国家及2004至2025年的时间跨度。数据以Parquet格式封装，并附有统一的模式描述，确保可追溯性与学术研究的便捷性。

使用方法

使用者可直接通过HuggingFace的`datasets`库以`load_dataset`函数加载该数据集，并转换为Pandas DataFrame进行后续分析。例如，可按国家代码筛选特定国家的观测值，或针对LU2指标按年份排序绘制时间序列图，以观察其演变趋势。亦可利用透视表功能将数据重塑为国家×年份的矩阵形式，便于进行跨国的横向比较。数据集的设计兼顾了时间序列预测、表格分类与回归任务，为劳动政策研究、经济模型构建及可持续发展目标（SDG）评估提供了结构化的数据基础。

背景与挑战

背景概述

劳动力市场的多维衡量指标是评估经济活动与劳动资源分配效率的重要工具，传统上以失业率为核心的指标体系难以全面反映劳动资源闲置的复杂现实。国际劳工组织（ILO）作为全球劳动统计的权威机构，基于多年的劳动力调查数据整合，于其ILOSTAT数据库中推出了LUU_XLU2_SEX_DSB_RT这一复合性指标，旨在量化时间相关就业不足与失业并存（LU2）的劳工闲置状况。该数据集由Electric Sheep Europe于2025年重新整理并发布至HuggingFace平台，覆盖2004至2025年间31个欧洲国家的4512条观测记录。通过按性别与残疾状况进行交叉分层，该数据集突破了传统统计的单一维度，为研究者深入理解欧洲不同社会群体在劳动市场中的弱势地位提供了标准化且可公开访问的微观宏观数据桥梁。

当前挑战

该数据集所应对的核心领域挑战在于：传统的失业率定义（如国际劳工会议标准ICLS）无法涵盖因工时不足而被动寻求额外工作的时间相关就业不足人群，导致对劳动资源闲置程度的系统性低估，尤其是对残疾人群体的统计盲区尤为显著。在构建过程中，数据整合面临多源异构性与可比性难题，因各成员国劳动力调查的抽样设计、问卷措辞与统计口径存在差异，ILO需通过复杂的回溯性调整与元数据标记来确保跨国家跨年份的一致性。此外，观测值中的可靠性标注（如‘unreliable’状态及方法修订断点提示）揭示了微观数据采集中难以避免的测量误差与制度变迁影响，要求使用者具备严谨的统计后处理与质量评估能力。

常用场景

经典使用场景

在劳动经济学与社会政策研究领域，准确度量劳动力市场的闲置程度是理解经济健康与就业质量的关键。该数据集作为ILOSTAT官方数据在欧盟地区的精炼版本，提供了按性别与残疾状况分层的非充分就业与失业综合率（LU2），涵盖2004至2025年间31个欧洲国家的4512条年度观测记录。其经典使用场景集中于构建跨国面板数据模型，用于追踪劳动力市场在时间维度上的动态变化，揭示不同性别群体及残疾人群在时间相关非充分就业与失业之间的分布特征，为评估各国劳动政策的效果提供实证基础。

解决学术问题

该数据集直击劳动经济学中一个长期存在的结构性难题：传统失业率指标无法完整刻画劳动力市场的隐性闲置状态，尤其当劳动者被迫从事低于其工作时间意愿的职位时。通过整合时间相关非充分就业与失业率，LU2指标弥补了标准失业统计的盲区，使研究者能够量化‘被忽视的就业不足’现象。此外，数据按性别与残疾状况进行精细分层，突破了以往宏观劳动力研究中因维度缺失而导致的群体异质性分析困境，能够系统探讨社会弱势群体面临的多重劳动市场壁垒，为包容性增长理论提供了关键的实证支撑。

实际应用

在实际应用层面，该数据集成为国际组织与各国劳动部门开展劳动力市场监测与政策评估的核心工具。欧盟统计局及国际劳工组织可利用其对成员国进行横向比较，识别非充分就业高发国别，并追溯其与劳动力市场法规、社会保障体系之间的关联。同时，数据对性别与残疾维度的详尽分类，使非政府组织与政策智库能够精准定位最易受劳动市场排斥的群体，为设计定向职业培训、灵活工时制度及反歧视法案提供量化依据。此外，数据集的高时间分辨率与标准化结构，支持其直接接入机器学习预测模型，用于预警劳动市场过热或衰退的风险。

数据集最近研究

最新研究方向

在劳动力市场包容性研究的前沿，该数据集聚焦于欧洲31国2004至2025年间按性别与残疾状态划分的时间相关就业不足与失业综合率（LU2），为探讨残障群体在劳动力市场中的边缘化处境及其动态演变提供了关键实证基础。依托国际劳工组织ILOSTAT的标准化框架，数据集的精细化分层设计使得研究者能深入剖析结构性就业障碍与性别交叉不平等，尤其在新冠疫情后全球劳动力复苏与欧盟“2030包容性就业目标”的政策议程下，该数据成为量化评估就业援助政策效能、追踪残障劳动力参与落差及推动循证决策的稀缺资源，其跨年度序列亦为时间序列预测劳动力市场弹性与可持续发展目标（SDG 8）监测注入了高精度分析动能。